Receivers used in communication, navigation, radar, and other sensor applications can suffer from intentional or unintentional interference. In such systems, it can be useful to use signal processing methods to reduce the effects of an interference signal from those of a desired signal.
In some systems, properties of the interference signal are not known a priori. In such cases, it can be desirable to use a blind technique, in which properties of the desired signal also may not necessarily be a priori known. A number of previously known blind single antenna interference suppression methods have been developed in the time domain and frequency-domain of a signal described in the complex in-phase (I) and quadrature (Q) domain. Some interference suppression techniques excise the interference in a domain in which the interference signal can be separated from the desired signal, and selectively excised. Except for time domain pulse blanking, such methods can assume a particular frequency-domain or time/frequency transform, or utilize adaptive notch filtering in which the interference can be removed by use of a notch filter or frequency domain excision filter in the I/Q domain of the signal. While such approaches potentially can work well for narrowband interference, such approaches can fail when the interference signal spectrally overlaps the desired signal, e.g., is spectrally matched to the desired signal or has a bandwidth that overlaps some or all of the desired signal. For example, previously known notch filtering or frequency domain excision approaches can rely on the interference signal spectrally overlapping only a portion of the desired signal in the I/Q domain. If the interference signal is spectrally matched to the desired signal, then such narrowband excision techniques will remove the desired signal as well as the interference.
Some approaches for reducing interference are non-blind, e.g., are based upon a priori knowledge of the desired signal or of the interference signal. Some of such techniques are sometimes referred to as multi-user detection (MUD) techniques. In such approaches, a copy of the interference can be reconstructed in the receiver, including the precise amplitude, phase, and timing of the interference. The reconstructed interference then is subtracted from the incoming signal. Such approaches can require some time to track any interference that changes form or rapidly varies. Such variation can make it difficult or impossible to reconstruct the interference in a receiver. Moreover, multiple interferers requires removal of each interferer in the presence of the others making it very difficult to reconstruct precise phase, timing and amplitude of each interferer, One such technique to remove the effects of multiple interferers is successive interference cancellation and will fail when the interferers get too close in amplitude.
Although adaptive antenna-array can be used to null out a matched spectral or overlapping interference signal in the spatial domain, where the signal and interference are separated from one another along different spatial directions, spatial domain nulling and beam-forming approaches can be costly and may not support a vast array of single element receiver implementations, such as handheld receivers.
Numerous approaches have been devised to mitigate strong constant envelope co-channel interference received using a single receive antenna. Maximum likelihood sequence estimation (MLSE) in the presence of constant envelope interference is one known technique with a reasonably simple hardware implementation. See, for example, Hui et al., “Maximum Likelihood Sequence Estimation in the Presence of Constant Envelope Interference,” IEEE Vehicular Technology Conference 2: 1060-1064 (2003), the entire contents of which are incorporated by reference herein. However, the MLSE algorithm or hardware must be customized for the specific desired signal.
Another approach uses an adaptive filter to cancel interference caused by a constant envelope signal. This adaptive approach requires time to converge, and even then a narrow band signal buried beneath a wide-band strong interference signal might not be recovered because the steady state transfer function is frequency selective. See, for example, Ferrara, “A Method for Cancelling Interference from a Constant Envelope Signal,” IEEE Transactions on Acoustics, Speech, and Signal Processing 33(1): 316-319 (1985), the entire contents of which are incorporated by reference herein.
A different approach maps a complex received signal into polar coordinates. Then a fast Fourier transform (FFT) is computed on a block of magnitude samples. The spectrum of the magnitude samples is then excised. An inverse FFT (iFFT) then transforms the excised spectrum into the time domain. Such an approach does not require convergence time or any parameters of the weak signal, and can cancel many interference signals automatically. See, for example, Henttu, “A New Interference Suppression Algorithm Against Broadband Constant Envelope Interference,” IEEE Milcom 2: 742-746 (2000), the entire contents of which are incorporated by reference herein. However, such an approach can be computationally complex, and also relies upon the interference having an approximately constant envelope. However, the envelope of some interference can vary by more than 3 dB.
A technique that may be used to mitigate multiple interferers, for example, is successive interference cancellation. This technique, however, requires knowledge of each interferer and that the difference in power of each interferer is sufficient that the strongest interferer can be successively estimated, demodulated and subtracted from the remaining interferers, wherein the process is repeated until all interferers are removed. Without prior knowledge of the interferers or if the interferers are too close in power, successive interference cancellation will fail.
Joint demodulators can sometimes mitigate multiple interferers by demodulating both signals together in a statistically optimum manner. Such techniques can be computationally complex and do not work well with multiple interferers due to rapidly increasing complexity as the number of interferers increases.
In either case, a demodulator for one desired signal type can then require demodulators for many different undesired signal types. As new signals emerge, algorithms must be updated. Unknown signals, such as proprietary waveforms, can render successive interference cancellation or joint demodulators impractical.
Thus, what is needed are improved systems and methods for reducing interference.